Diverse Non-Genetic Allele Specific Expression Effects Shape Genetic Architecture at the Cellular Level in the Mammalian Brain [Primate Daughters]
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ABSTRACT:
ORGANISM(S): Macaca fascicularis
PROVIDER: GSE93786 | GEO | 2017/02/28
SECONDARY ACCESSION(S): PRJNA362511
REPOSITORIES: GEO
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Project description:Interactions between genetic and epigenetic effects shape brain function, behavior, and the risk for mental illness. Random X inactivation and genomic imprinting are epigenetic allelic effects that are well known to influence genetic architecture and disease risk. Less is known about the nature, prevalence, and conservation of other potential epigenetic allelic effects in vivo in the mouse and primate brain. Here we devise genomics, in situ hybridization, and mouse genetics strategies to uncover diverse allelic effects in the brain that are not caused by imprinting or genetic variation. We found allelic effects that are developmental stage and cell type specific, that are prevalent in the neonatal brain, and that cause mosaics of monoallelic brain cells that differentially express wild-type and mutant alleles for heterozygous mutations. Finally, we show that diverse non-genetic allelic effects that impact mental illness risk genes exist in the macaque and human brain. Our findings have potential implications for mammalian brain genetics. VIDEO ABSTRACT.
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Project description:Diverse Non-Genetic Allele Specific Expression Effects Shape Genetic Architecture at the Cellular Level in the Mammalian Brain
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Project description:BackgroundBackfat thickness is an important carcass composition trait for pork production and is commonly included in swine breeding programmes. In this paper, we report the results of a large genome-wide association study for backfat thickness using data from eight lines of diverse genetic backgrounds.MethodsData comprised 275,590 pigs from eight lines with diverse genetic backgrounds (breeds included Large White, Landrace, Pietrain, Hampshire, Duroc, and synthetic lines) genotyped and imputed for 71,324 single-nucleotide polymorphisms (SNPs). For each line, we estimated SNP associations using a univariate linear mixed model that accounted for genomic relationships. SNPs with significant associations were identified using a threshold of p < 10-6 and used to define genomic regions of interest. The proportion of genetic variance explained by a genomic region was estimated using a ridge regression model.ResultsWe found significant associations with backfat thickness for 264 SNPs across 27 genomic regions. Six genomic regions were detected in three or more lines. The average estimate of the SNP-based heritability was 0.48, with estimates by line ranging from 0.30 to 0.58. The genomic regions jointly explained from 3.2 to 19.5% of the additive genetic variance of backfat thickness within a line. Individual genomic regions explained up to 8.0% of the additive genetic variance of backfat thickness within a line. Some of these 27 genomic regions also explained up to 1.6% of the additive genetic variance in lines for which the genomic region was not statistically significant. We identified 64 candidate genes with annotated functions that can be related to fat metabolism, including well-studied genes such as MC4R, IGF2, and LEPR, and more novel candidate genes such as DHCR7, FGF23, MEDAG, DGKI, and PTN.ConclusionsOur results confirm the polygenic architecture of backfat thickness and the role of genes involved in energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggest that several less well-understood metabolic pathways contribute to backfat development, such as those of phosphate, calcium, and vitamin D homeostasis.
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